Version 1
: Received: 9 April 2023 / Approved: 12 April 2023 / Online: 12 April 2023 (08:19:41 CEST)
How to cite:
Kandel, A.; Prakhar, U.; Kaur, M.; Nand, P.; Rakesh, N. Plant Leaves Disease Detection using Deep Learning. Preprints2023, 2023040259. https://doi.org/10.20944/preprints202304.0259.v1
Kandel, A.; Prakhar, U.; Kaur, M.; Nand, P.; Rakesh, N. Plant Leaves Disease Detection using Deep Learning. Preprints 2023, 2023040259. https://doi.org/10.20944/preprints202304.0259.v1
Kandel, A.; Prakhar, U.; Kaur, M.; Nand, P.; Rakesh, N. Plant Leaves Disease Detection using Deep Learning. Preprints2023, 2023040259. https://doi.org/10.20944/preprints202304.0259.v1
APA Style
Kandel, A., Prakhar, U., Kaur, M., Nand, P., & Rakesh, N. (2023). Plant Leaves Disease Detection using Deep Learning. Preprints. https://doi.org/10.20944/preprints202304.0259.v1
Chicago/Turabian Style
Kandel, A., Parma Nand and Nitin Rakesh. 2023 "Plant Leaves Disease Detection using Deep Learning" Preprints. https://doi.org/10.20944/preprints202304.0259.v1
Abstract
Advances in computing technology provide an opportunity to grow and develop an effective crop protection system. This study is a new way of diagnosing plant diseases using a deep and convolutional neural network. Deep learning is the strengthened method of machine learning. It uses a neural network that imitates like a human brain and gains certain information. Although powerful, its training process necessitates millions of tagged data points. The study's Methodology consists of three key stages: data acquisition, pre-processing, and Model Building.
Keywords
TensorFlow; Convolutional Neural Network; Machine Learning; Artificial Intelligence; Deep Learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.